Why Read This Book

You will get a rigorous, systems-oriented grounding in signals theory while seeing how theory maps to real DSP practice through integrated MATLAB examples and interactive solutions. The book connects core transforms and filter design to concrete applications (audio, speech, radar, remote sensing, biomedical and seismology), so you learn both the math and how to apply it to real signal-processing problems.

Who Will Benefit

Upper-level undergraduates, graduate students, and practicing engineers who want a solid signals-and-systems foundation plus hands-on MATLAB-based exposure to DSP algorithms and application case studies.

Level: Intermediate — Prerequisites: Single-variable calculus, basic linear algebra, introductory differential equations, and basic programming familiarity (MATLAB or another scripting language recommended).

Key Takeaways

• Analyze continuous- and discrete-time signals and linear time-invariant systems using Fourier, Laplace, and Z-transforms.
• Design and evaluate digital filters (FIR and IIR) and implement them with MATLAB for audio, speech, and communications tasks.
• Compute and apply the FFT and spectral analysis techniques to real-world signals and measurement data.
• Apply adaptive filtering and statistical signal-processing methods to noise suppression, system identification, and parameter estimation.
• Use wavelet transforms for time–frequency analysis and implement wavelet-based processing workflows in MATLAB.
• Relate theoretical signal-processing concepts to application domains such as radar, remote sensing, radio astronomy, biomedical signals, and seismology.

Topics Covered

1. 1. Signals: Classifications and Representations
2. 2. Systems and Properties; Linearity and Time-Invariance
3. 3. Time-Domain Analysis of LTI Systems; Convolution
4. 4. Fourier Series and Fourier Transform (Continuous-Time)
5. 5. Laplace Transform and System Function
6. 6. Sampling Theorem and Discrete-Time Signals
7. 7. Z-Transform and Discrete-Time System Analysis
8. 8. Discrete-Time Fourier Transform and Frequency Analysis
9. 9. Fast Fourier Transform and Efficient Spectral Methods
10. 10. Digital Filter Design: FIR and IIR Techniques
11. 11. State-Space Methods and Multirate Systems
12. 12. Statistical Signal Processing and Random Signals
13. 13. Adaptive Filtering and LMS/Recursive Algorithms
14. 14. Wavelets and Time–Frequency Analysis
15. 15. Applications: Audio/Speech, Radar, Communications, Remote Sensing, Biomedical, and Seismology (with MATLAB examples)

Languages, Platforms & Tools

How It Compares

Covers similar fundamental ground to Oppenheim & Willsky's Signals and Systems but places more emphasis on MATLAB integration and application case studies; compared with Proakis & Manolakis, it emphasizes systems theory and application-driven examples rather than exhaustive DSP algorithm derivations.